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EBioMedicine. 2019 Jan;39:109-117. doi: 10.1016/j.ebiom.2018.12.033. Epub 2018 Dec 23.

Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery disease.

Author information

1
Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
2
HorAIzon BV, Rotterdam, the Netherlands; Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
3
Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
4
Icahn School of Medicine, Mount Sinai Hospital, NY, New York, United States.
5
Dalio Institute for Cardiovascular Imaging, Weill-Cornell Medical College, NY, New York, United States.
6
Department of Medicine and Radiology, University of British Columbia, Vancouver, Canada.
7
Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
8
Department of Bioengineering, Stanford University, Stanford, CA, United States.
9
Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Wallenberg Laboratory, University of Gothenberg, Gothenberg, Sweden; Department of Internal Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
10
Deutsches Herzzentrum München, Technische Universität München, Munich, Germany; DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Munich, Germany.
11
Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. Electronic address: p.knaapen@vumc.nl.

Abstract

BACKGROUND:

Risk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA).

METHODS:

Patients with suspected CAD (n = 203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers.

FINDINGS:

A total of 196 patients with analyzable protein levels (n = 332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79 ± 0·01, outperforming prediction with generally available clinical characteristics (AUC = 0·65 ± 0·04, p < 0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC = 0·85 ± 0·05), again outperforming prediction with generally available characteristics (AUC = 0·70 ± 0·04, p < 0·05).

INTERPRETATION:

Using machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies. FUND: This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.

KEYWORDS:

Biomarkers; Coronary artery disease; Coronary computed tomography angiography; Proteomics; Risk assessment

PMID:
30587458
PMCID:
PMC6355456
DOI:
10.1016/j.ebiom.2018.12.033
[Indexed for MEDLINE]
Free PMC Article

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